





import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY


@is_pipeline_test
class     A     (unittest.TestCase       ):
			'''simple docstring'''




			__lowerCamelCase      :			Optional[Any]							     =       MODEL_FOR_MASKED_LM_MAPPING
			__lowerCamelCase      :			List[Any]							     =       TF_MODEL_FOR_MASKED_LM_MAPPING






			def   a_			(     self  :       Optional[int]  )      ->      Dict:







					"""simple docstring"""






					super().tearDown()
					# clean-up as much as possible GPU memory occupied by PyTorch
					gc.collect()
					if is_torch_available():
							import torch

							torch.cuda.empty_cache()






			@require_tf
			def   a_			(     self  :       Optional[Any]  )      ->      str:







					"""simple docstring"""






					A__     =  pipeline(task="""fill-mask"""				,      model="""sshleifer/tiny-distilroberta-base"""				,      top_k=2				,      framework="""tf"""  )
					A__     =  unmasker("""My name is <mask>"""  )
					self.assertEqual(
					    nested_simplify(_lowerCamelCase				,      decimals=6  )				,      [
					        {"""sequence""": """My name is grouped""", """score""": 2.1e-05, """token""": 3_80_15, """token_str""": """ grouped"""},
					        {"""sequence""": """My name is accuser""", """score""": 2.1e-05, """token""": 2_55_06, """token_str""": """ accuser"""},
					    ]				,      )

					A__     =  unmasker("""The largest city in France is <mask>"""  )
					self.assertEqual(
					    nested_simplify(_lowerCamelCase				,      decimals=6  )				,      [
					        {
					            """sequence""": """The largest city in France is grouped""",
					            """score""": 2.1e-05,
					            """token""": 3_80_15,
					            """token_str""": """ grouped""",
					        },
					        {
					            """sequence""": """The largest city in France is accuser""",
					            """score""": 2.1e-05,
					            """token""": 2_55_06,
					            """token_str""": """ accuser""",
					        },
					    ]				,      )

					A__     =  unmasker("""My name is <mask>"""				,      targets=[""" Patrick""", """ Clara""", """ Teven"""]				,      top_k=3  )
					self.assertEqual(
					    nested_simplify(_lowerCamelCase				,      decimals=6  )				,      [
					        {"""sequence""": """My name is Clara""", """score""": 2e-05, """token""": 1_36_06, """token_str""": """ Clara"""},
					        {"""sequence""": """My name is Patrick""", """score""": 2e-05, """token""": 34_99, """token_str""": """ Patrick"""},
					        {"""sequence""": """My name is Te""", """score""": 1.9e-05, """token""": 29_41, """token_str""": """ Te"""},
					    ]				,      )






			@require_torch
			def   a_			(     self  :       int  )      ->      str:







					"""simple docstring"""






					A__     =  pipeline(task="""fill-mask"""				,      model="""sshleifer/tiny-distilroberta-base"""				,      top_k=2				,      framework="""pt"""  )

					A__     =  unmasker("""My name is <mask>"""  )
					self.assertEqual(
					    nested_simplify(_lowerCamelCase				,      decimals=6  )				,      [
					        {"""sequence""": """My name is Maul""", """score""": 2.2e-05, """token""": 3_56_76, """token_str""": """ Maul"""},
					        {"""sequence""": """My name isELS""", """score""": 2.2e-05, """token""": 1_64_16, """token_str""": """ELS"""},
					    ]				,      )

					A__     =  unmasker("""The largest city in France is <mask>"""  )
					self.assertEqual(
					    nested_simplify(_lowerCamelCase				,      decimals=6  )				,      [
					        {
					            """sequence""": """The largest city in France is Maul""",
					            """score""": 2.2e-05,
					            """token""": 3_56_76,
					            """token_str""": """ Maul""",
					        },
					        {"""sequence""": """The largest city in France isELS""", """score""": 2.2e-05, """token""": 1_64_16, """token_str""": """ELS"""},
					    ]				,      )

					A__     =  unmasker("""My name is <mask>"""				,      targets=[""" Patrick""", """ Clara""", """ Teven"""]				,      top_k=3  )
					self.assertEqual(
					    nested_simplify(_lowerCamelCase				,      decimals=6  )				,      [
					        {"""sequence""": """My name is Patrick""", """score""": 2.1e-05, """token""": 34_99, """token_str""": """ Patrick"""},
					        {"""sequence""": """My name is Te""", """score""": 2e-05, """token""": 29_41, """token_str""": """ Te"""},
					        {"""sequence""": """My name is Clara""", """score""": 2e-05, """token""": 1_36_06, """token_str""": """ Clara"""},
					    ]				,      )

					A__     =  unmasker("""My name is <mask> <mask>"""				,      top_k=2  )

					self.assertEqual(
					    nested_simplify(_lowerCamelCase				,      decimals=6  )				,      [
					        [
					            {
					                """score""": 2.2e-05,
					                """token""": 3_56_76,
					                """token_str""": """ Maul""",
					                """sequence""": """<s>My name is Maul<mask></s>""",
					            },
					            {"""score""": 2.2e-05, """token""": 1_64_16, """token_str""": """ELS""", """sequence""": """<s>My name isELS<mask></s>"""},
					        ],
					        [
					            {
					                """score""": 2.2e-05,
					                """token""": 3_56_76,
					                """token_str""": """ Maul""",
					                """sequence""": """<s>My name is<mask> Maul</s>""",
					            },
					            {"""score""": 2.2e-05, """token""": 1_64_16, """token_str""": """ELS""", """sequence""": """<s>My name is<mask>ELS</s>"""},
					        ],
					    ]				,      )






			@require_torch_gpu
			def   a_			(     self  :       str  )      ->      List[str]:







					"""simple docstring"""






					A__     =  pipeline("""fill-mask"""				,      model="""hf-internal-testing/tiny-random-distilbert"""				,      device=0				,      framework="""pt"""  )

					# convert model to fp16
					pipe.model.half()

					A__     =  pipe("""Paris is the [MASK] of France."""  )
					# We actually don't care about the result, we just want to make sure
					# it works, meaning the float16 tensor got casted back to float32
					# for postprocessing.
					self.assertIsInstance(_lowerCamelCase				,      _lowerCamelCase  )






			@slow
			@require_torch
			def   a_			(     self  :       Any  )      ->      Union[str, Any]:







					"""simple docstring"""






					A__     =  pipeline(task="""fill-mask"""				,      model="""distilroberta-base"""				,      top_k=2				,      framework="""pt"""  )
					self.run_large_test(_lowerCamelCase  )






			@slow
			@require_tf
			def   a_			(     self  :       Optional[int]  )      ->      Union[str, Any]:







					"""simple docstring"""






					A__     =  pipeline(task="""fill-mask"""				,      model="""distilroberta-base"""				,      top_k=2				,      framework="""tf"""  )
					self.run_large_test(_lowerCamelCase  )






			def   a_			(     self  :       Any				,      __lowerCAmelCase  :       Optional[Any]  )      ->      Optional[Any]:







					"""simple docstring"""






					A__     =  unmasker("""My name is <mask>"""  )
					self.assertEqual(
					    nested_simplify(_lowerCamelCase  )				,      [
					        {"""sequence""": """My name is John""", """score""": 0.0_0_8, """token""": 6_10, """token_str""": """ John"""},
					        {"""sequence""": """My name is Chris""", """score""": 0.0_0_7, """token""": 15_73, """token_str""": """ Chris"""},
					    ]				,      )
					A__     =  unmasker("""The largest city in France is <mask>"""  )
					self.assertEqual(
					    nested_simplify(_lowerCamelCase  )				,      [
					        {
					            """sequence""": """The largest city in France is Paris""",
					            """score""": 0.2_5_1,
					            """token""": 22_01,
					            """token_str""": """ Paris""",
					        },
					        {
					            """sequence""": """The largest city in France is Lyon""",
					            """score""": 0.2_1_4,
					            """token""": 1_27_90,
					            """token_str""": """ Lyon""",
					        },
					    ]				,      )

					A__     =  unmasker("""My name is <mask>"""				,      targets=[""" Patrick""", """ Clara""", """ Teven"""]				,      top_k=3  )
					self.assertEqual(
					    nested_simplify(_lowerCamelCase  )				,      [
					        {"""sequence""": """My name is Patrick""", """score""": 0.0_0_5, """token""": 34_99, """token_str""": """ Patrick"""},
					        {"""sequence""": """My name is Clara""", """score""": 0.0_0_0, """token""": 1_36_06, """token_str""": """ Clara"""},
					        {"""sequence""": """My name is Te""", """score""": 0.0_0_0, """token""": 29_41, """token_str""": """ Te"""},
					    ]				,      )






			@require_torch
			def   a_			(     self  :       int  )      ->      Optional[Any]:







					"""simple docstring"""






					A__     =  pipeline(task="""fill-mask"""				,      model="""sshleifer/tiny-distilroberta-base"""				,      framework="""pt"""  )
					A__     =  None
					A__     =  None
					self.run_pipeline_test(_lowerCamelCase				,      []  )






			@require_tf
			def   a_			(     self  :       Dict  )      ->      Optional[Any]:







					"""simple docstring"""






					A__     =  pipeline(task="""fill-mask"""				,      model="""sshleifer/tiny-distilroberta-base"""				,      framework="""tf"""  )
					A__     =  None
					A__     =  None
					self.run_pipeline_test(_lowerCamelCase				,      []  )






			def   a_			(     self  :       int				,      __lowerCAmelCase  :       Any				,      __lowerCAmelCase  :       Tuple				,      __lowerCAmelCase  :       List[str]  )      ->      Tuple:







					"""simple docstring"""






					if tokenizer is None or tokenizer.mask_token_id is None:
							self.skipTest("""The provided tokenizer has no mask token, (probably reformer or wav2vec2)"""  )

					A__     =  FillMaskPipeline(model=_lowerCamelCase				,      tokenizer=_lowerCamelCase  )
					A__     =  [
					    f'This is another {tokenizer.mask_token} test',
					]
					return fill_masker, examples






			def   a_			(     self  :       int				,      __lowerCAmelCase  :       Union[str, Any]				,      __lowerCAmelCase  :       List[str]  )      ->      Dict:







					"""simple docstring"""






					A__     =  fill_masker.tokenizer
					A__     =  fill_masker.model

					A__     =  fill_masker(
					    f'This is a {tokenizer.mask_token}'				,      )
					self.assertEqual(
					    _lowerCamelCase				,      [
					        {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					        {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					        {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					        {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					        {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					    ]				,      )

					A__     =  fill_masker([f'This is a {tokenizer.mask_token}']  )
					self.assertEqual(
					    _lowerCamelCase				,      [
					        {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					        {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					        {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					        {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					        {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					    ]				,      )

					A__     =  fill_masker([f'This is a {tokenizer.mask_token}', f'Another {tokenizer.mask_token} great test.']  )
					self.assertEqual(
					    _lowerCamelCase				,      [
					        [
					            {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					            {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					            {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					            {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					            {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					        ],
					        [
					            {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					            {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					            {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					            {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					            {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					        ],
					    ]				,      )

					with self.assertRaises(_lowerCamelCase  ):
							fill_masker([None]  )
					# No mask_token is not supported
					with self.assertRaises(_lowerCamelCase  ):
							fill_masker("""This is"""  )

					self.run_test_top_k(_lowerCamelCase				,      _lowerCamelCase  )
					self.run_test_targets(_lowerCamelCase				,      _lowerCamelCase  )
					self.run_test_top_k_targets(_lowerCamelCase				,      _lowerCamelCase  )
					self.fill_mask_with_duplicate_targets_and_top_k(_lowerCamelCase				,      _lowerCamelCase  )
					self.fill_mask_with_multiple_masks(_lowerCamelCase				,      _lowerCamelCase  )






			def   a_			(     self  :       int				,      __lowerCAmelCase  :       Tuple				,      __lowerCAmelCase  :       Any  )      ->      List[str]:







					"""simple docstring"""






					A__     =  tokenizer.get_vocab()
					A__     =  sorted(vocab.keys()  )[:2]
					# Pipeline argument
					A__     =  FillMaskPipeline(model=_lowerCamelCase				,      tokenizer=_lowerCamelCase				,      targets=_lowerCamelCase  )
					A__     =  fill_masker(f'This is a {tokenizer.mask_token}'  )
					self.assertEqual(
					    _lowerCamelCase				,      [
					        {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					        {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					    ]				,      )
					A__     =  {vocab[el] for el in targets}
					self.assertEqual({el["""token"""] for el in outputs}				,      _lowerCamelCase  )
					A__     =  [tokenizer.decode([x]  ) for x in target_ids]
					self.assertEqual({el["""token_str"""] for el in outputs}				,      set(_lowerCamelCase  )  )

					# Call argument
					A__     =  FillMaskPipeline(model=_lowerCamelCase				,      tokenizer=_lowerCamelCase  )
					A__     =  fill_masker(f'This is a {tokenizer.mask_token}'				,      targets=_lowerCamelCase  )
					self.assertEqual(
					    _lowerCamelCase				,      [
					        {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					        {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					    ]				,      )
					A__     =  {vocab[el] for el in targets}
					self.assertEqual({el["""token"""] for el in outputs}				,      _lowerCamelCase  )
					A__     =  [tokenizer.decode([x]  ) for x in target_ids]
					self.assertEqual({el["""token_str"""] for el in outputs}				,      set(_lowerCamelCase  )  )

					# Score equivalence
					A__     =  fill_masker(f'This is a {tokenizer.mask_token}'				,      targets=_lowerCamelCase  )
					A__     =  [top_mask["""token_str"""] for top_mask in outputs]
					A__     =  [top_mask["""score"""] for top_mask in outputs]

					# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
					if set(_lowerCamelCase  ) == set(_lowerCamelCase  ):
							A__     =  fill_masker(f'This is a {tokenizer.mask_token}'				,      targets=_lowerCamelCase  )
							A__     =  [top_mask["""score"""] for top_mask in unmasked_targets]
							self.assertEqual(nested_simplify(_lowerCamelCase  )				,      nested_simplify(_lowerCamelCase  )  )

					# Raises with invalid
					with self.assertRaises(_lowerCamelCase  ):
							A__     =  fill_masker(f'This is a {tokenizer.mask_token}'				,      targets=[]  )
					# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
					if "" not in tokenizer.get_vocab():
							with self.assertRaises(_lowerCamelCase  ):
									A__     =  fill_masker(f'This is a {tokenizer.mask_token}'				,      targets=[""""""]  )
							with self.assertRaises(_lowerCamelCase  ):
									A__     =  fill_masker(f'This is a {tokenizer.mask_token}'				,      targets=""""""  )






			def   a_			(     self  :       int				,      __lowerCAmelCase  :       Optional[int]				,      __lowerCAmelCase  :       Optional[int]  )      ->      Optional[int]:







					"""simple docstring"""






					A__     =  FillMaskPipeline(model=_lowerCamelCase				,      tokenizer=_lowerCamelCase				,      top_k=2  )
					A__     =  fill_masker(f'This is a {tokenizer.mask_token}'  )
					self.assertEqual(
					    _lowerCamelCase				,      [
					        {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					        {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					    ]				,      )

					A__     =  FillMaskPipeline(model=_lowerCamelCase				,      tokenizer=_lowerCamelCase  )
					A__     =  fill_masker(f'This is a {tokenizer.mask_token}'				,      top_k=2  )
					self.assertEqual(
					    _lowerCamelCase				,      [
					        {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					        {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					    ]				,      )
					self.assertEqual(nested_simplify(_lowerCamelCase  )				,      nested_simplify(_lowerCamelCase  )  )






			def   a_			(     self  :       List[Any]				,      __lowerCAmelCase  :       Optional[Any]				,      __lowerCAmelCase  :       List[str]  )      ->      List[Any]:







					"""simple docstring"""






					A__     =  tokenizer.get_vocab()
					A__     =  FillMaskPipeline(model=_lowerCamelCase				,      tokenizer=_lowerCamelCase  )

					# top_k=2, ntargets=3
					A__     =  sorted(vocab.keys()  )[:3]
					A__     =  fill_masker(f'This is a {tokenizer.mask_token}'				,      top_k=2				,      targets=_lowerCamelCase  )

					# If we use the most probably targets, and filter differently, we should still
					# have the same results
					A__     =  [el["""token_str"""] for el in sorted(_lowerCamelCase				,      key=lambda __lowerCAmelCase  : x["score"]				,      reverse=_lowerCamelCase  )]
					# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
					if set(_lowerCamelCase  ).issubset(_lowerCamelCase  ):
							A__     =  fill_masker(f'This is a {tokenizer.mask_token}'				,      top_k=3				,      targets=_lowerCamelCase  )
							# They should yield exactly the same result
							self.assertEqual(nested_simplify(_lowerCamelCase  )				,      nested_simplify(_lowerCamelCase  )  )






			def   a_			(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       List[str]				,      __lowerCAmelCase  :       Optional[Any]  )      ->      Tuple:







					"""simple docstring"""






					A__     =  FillMaskPipeline(model=_lowerCamelCase				,      tokenizer=_lowerCamelCase  )
					A__     =  tokenizer.get_vocab()
					# String duplicates + id duplicates
					A__     =  sorted(vocab.keys()  )[:3]
					A__     =  [targets[0], targets[1], targets[0], targets[2], targets[1]]
					A__     =  fill_masker(f'My name is {tokenizer.mask_token}'				,      targets=_lowerCamelCase				,      top_k=10  )

					# The target list contains duplicates, so we can't output more
					# than them
					self.assertEqual(len(_lowerCamelCase  )				,      3  )







			def   a_			(     self  :       List[Any]				,      __lowerCAmelCase  :       List[str]				,      __lowerCAmelCase  :       List[Any]  )      ->      Optional[int]:







					"""simple docstring"""






					A__     =  FillMaskPipeline(model=_lowerCamelCase				,      tokenizer=_lowerCamelCase  )

					A__     =  fill_masker(
					    f'This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}'				,      top_k=2  )
					self.assertEqual(
					    _lowerCamelCase				,      [
					        [
					            {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					            {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					        ],
					        [
					            {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					            {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					        ],
					        [
					            {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					            {"""sequence""": ANY(_lowerCamelCase  ), """score""": ANY(_lowerCamelCase  ), """token""": ANY(_lowerCamelCase  ), """token_str""": ANY(_lowerCamelCase  )},
					        ],
					    ]				,      )




"""simple docstring"""


from __future__ import annotations

from typing import Any






class   __UpperCAmelCase		:

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase = 0 ):
										lowerCamelCase__   , lowerCamelCase__   =row, column
										lowerCamelCase__   =[[default_value for c in range(_lowerCamelCase )] for r in range(_lowerCamelCase )]

				def __str__( self ):
										lowerCamelCase__   =F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

										# Make string identifier
										lowerCamelCase__   =0
										for row_vector in self.array:
																for obj in row_vector:
																						lowerCamelCase__   =max(_lowerCamelCase      ,					len(str(_lowerCamelCase ) ) )
										lowerCamelCase__   =F'''%{max_element_length}s'''

										# Make string and return
										def single_line(_lowerCamelCase ) -> str:
																nonlocal string_format_identifier
																lowerCamelCase__   ="["
																line += ", ".join(string_format_identifier % (obj,) for obj in row_vector )
																line += "]"
																return line

										s += "\n".join(single_line(_lowerCamelCase ) for row_vector in self.array )
										return s

				def __repr__( self ):
										return str(self )

				def 						_a      ( self      ,					_lowerCamelCase ):
										if not (isinstance(_lowerCamelCase      ,					(list, tuple) ) and len(_lowerCamelCase ) == 2):
																return False
										elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
																return False
										else:
																return True

				def __getitem__( self      ,					_lowerCamelCase ):
										assert self.validate_indicies(_lowerCamelCase )
										return self.array[loc[0]][loc[1]]

				def __setitem__( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										assert self.validate_indicies(_lowerCamelCase )
										lowerCamelCase__   =value

				def __add__( self      ,					_lowerCamelCase ):
										assert isinstance(_lowerCamelCase      ,					_lowerCamelCase )
										assert self.row == another.row and self.column == another.column

										# Add
										lowerCamelCase__   =Matrix(self.row      ,					self.column )
										for r in range(self.row ):
																for c in range(self.column ):
																						lowerCamelCase__   =self[r, c] + another[r, c]
										return result

				def __neg__( self ):
										lowerCamelCase__   =Matrix(self.row      ,					self.column )
										for r in range(self.row ):
																for c in range(self.column ):
																						lowerCamelCase__   =-self[r, c]
										return result

				def __sub__( self      ,					_lowerCamelCase ):
										return self + (-another)

				def __mul__( self      ,					_lowerCamelCase ):

										if isinstance(_lowerCamelCase      ,					(int, float) ):  # Scalar multiplication
																lowerCamelCase__   =Matrix(self.row      ,					self.column )
																for r in range(self.row ):
																						for c in range(self.column ):
																												lowerCamelCase__   =self[r, c] * another
																return result
										elif isinstance(_lowerCamelCase      ,					_lowerCamelCase ):  # Matrix multiplication
																assert self.column == another.row
																lowerCamelCase__   =Matrix(self.row      ,					another.column )
																for r in range(self.row ):
																						for c in range(another.column ):
																												for i in range(self.column ):
																																		result[r, c] += self[r, i] * another[i, c]
																return result
										else:
																lowerCamelCase__   =F'''Unsupported type given for another ({type(_lowerCamelCase )})'''
																raise TypeError(_lowerCamelCase )

				def 						_a      ( self ):
										lowerCamelCase__   =Matrix(self.column      ,					self.row )
										for r in range(self.row ):
																for c in range(self.column ):
																						lowerCamelCase__   =self[r, c]
										return result






				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										assert isinstance(_lowerCamelCase      ,					_lowerCamelCase ) and isinstance(_lowerCamelCase      ,					_lowerCamelCase )
										assert self.row == self.column == u.row == v.row  # u, v should be column vector
										assert u.column == v.column == 1  # u, v should be column vector

										# Calculate
										lowerCamelCase__   =v.transpose()
										lowerCamelCase__   =(v_t * self * u)[0, 0] + 1
										if numerator_factor == 0:
																return None  # It's not invertable
										return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":





	def      lowerCamelCase_					(   )				->   None:




							'''simple docstring'''



							lowerCamelCase__   =Matrix(3  ,       3  ,       0							)
							for i in range(3							):
													lowerCamelCase__   =1
							print(F'''a^(-1) is {ainv}'''							)
							# u, v
							lowerCamelCase__   =Matrix(3  ,       1  ,       0							)
							lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =1, 2, -3
							lowerCamelCase__   =Matrix(3  ,       1  ,       0							)
							lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =4, -2, 5
							print(F'''u is {u}'''							)
							print(F'''v is {v}'''							)
							print(F'''uv^T is {u * v.transpose()}'''							)
							# Sherman Morrison
							print(F'''(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowerCAmelCase  ,       __lowerCAmelCase							)}'''							)





	def      lowerCamelCase_					(   )				->   None:




							'''simple docstring'''



							import doctest

							doctest.testmod()





	testa()


